ggplot2

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# Using the _tidyverse_

### Maximilian H.K. Hesselbarth

#### University of Michigan (EEB)

2022-03-25

---

# The _tidyverse_

.pull-left[

.ref[www.tidyverse.org]

]

.pull-right[

.ref[Wickham, H., Grolemund, G., 2016. R for Data Science, 1st ed. O’Reilly, Newton (USA).]

]

---

# Tidy data

1. Each **variable** forms a **column**.

2. Each **observation** forms a **row**.

3. Each **value** must have its own **cell**.

.ref[Wickham, H., 2014. Tidy Data. Journal of Statistical Software 59, 1–23. https://doi.org/10.18637/jss.v059.i10]

.ref[Illustration from the Openscapes blog Tidy Data for reproducibility, efficiency, and collaboration by Julia Lowndes and Allison Horst]

---

# Tidy data

.ref[Wickham, H., Grolemund, G., 2016. R for Data Science, 1st ed. O’Reilly, Newton (USA).]

---

# Install and load

```r
install.packages("tidyverse")
```

```r
library(tidyverse)
```

```
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
```

```
## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.1.6     ✓ dplyr   1.0.8
## ✓ tidyr   1.2.0     ✓ stringr 1.4.0
## ✓ readr   2.1.2     ✓ forcats 0.5.1
```

```
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
```

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# Core packages

`readr`   : Read rectangular data

`tibble`  : Modern re-imagining of data frames

`stringr` : Functions to work with strings (i.e. sequence of characters)

`forcats` : Functions to modify factors (i.e. categorical data)

`tidyr`   : Functions to tidy/reshape data

`dplyr`   : Functions for data manipulation

`purrr`   : Functional programming

`ggplot2` : Data visualization

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# Pipe operator `%>%`

- `f(x)` is equivalent to `x %>% f`

- `f(x, y)` is equivalent to `x %>% f(y)`

- `f(y, x)` is equivalent to `x %>% f(y, .)`

```r
set.seed(42)
x <- runif(n = 10)

*min(log(sort(x)))
## [1] -2.004953

*x %>% sort() %>% log() %>% min()
## [1] -2.004953

set.seed(42)
*10 %>% runif() %>% sort() %>% log() %>% min()
## [1] -2.004953
```

---

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# Palmer penguins dataset

.ref[Horst A.M., Hill A.P., Gorman K.B., 2020. palmerpenguins: Palmer Archipelago (Antarctica) penguin data. R package version 0.1.0.]

---

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# Read data: _readr_

- Reads automatically into `tibble`

- Different `readr::read_*()` functions for different data types

- Functions to write data `readr::write_*()`

- `readxl` as alternative for Excel data

```r
*df_penguins <- readr::read_csv("data/penguins_raw.csv") %>%
  magrittr::set_names(names(.) %>% stringr::str_remove_all(pattern = " ") %>% 
                        stringr::str_remove_all(pattern = "\$[^()]+\$") %>% 
                        stringr::str_to_lower())
## Rows: 344 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): studyName, Species, Region, Island, Stage, Individual ID, Clutch C...
## dbl  (7): Sample Number, Culmen Length (mm), Culmen Depth (mm), Flipper Leng...
## date (1): Date Egg
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
```

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# Tidy data: _tidyr_

- `pivot_longer()` & `pivot_wider()` most important functions to **reshape** data

.yellow[(I will come back to this later!)]

- Some functions to deal with `NA` values

```r
df_penguins <- tidyr::drop_na(df_penguins, bodymass)
```

- `nest()`/`unnest()` to organize data

```r
tidyr::nest(df_penguins, data = -island)
```

```
## # A tibble: 3 × 2
##   island    data               
##   <chr>     <list>             
## 1 Torgersen <tibble [51 × 16]> 
## 2 Biscoe    <tibble [167 × 16]>
## 3 Dream     <tibble [124 × 16]>
```

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# Wrangle data: _dplyr_

- `filter()` to subset **rows**; `select()` to subset **columns**

```r
*dplyr::filter(df_penguins, bodymass >= quantile(bodymass, 0.75), sex != "FEMALE") %>%
* dplyr::select_if(is.numeric) %>%
  head(5)
```

```
## # A tibble: 5 × 7
##   samplenumber culmenlength culmendepth flipperlength bodymass delta15n delta13c
##          <dbl>        <dbl>       <dbl>         <dbl>    <dbl>    <dbl>    <dbl>
## 1          110         43.2        19             197     4775     9.32    -25.5
## 2            2         50          16.3           230     5700     8.15    -25.4
## 3            4         50          15.2           218     5700     8.26    -25.4
## 4            5         47.6        14.5           215     5400     8.23    -25.5
## 5            8         46.7        15.3           219     5200     8.23    -25.4
```

- `pull()` to convert **one** column as vector

```r
*dplyr::pull(df_penguins, flipperlength) %>%
  head(10)
```

```
##  [1] 181 186 195 193 190 181 195 193 190 186
```

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# Wrangle data: _dplyr_

- `mutate()` to **create/modify** columns

- `case_when()` as vectorised **ifelse** statements

- `slice()` to subset **rows** by position

```r
dplyr::select(df_penguins, individualid, species, culmenlength) %>%
* dplyr::mutate(culmenlength_cm = culmenlength / 10,
                culmenlenth_class = 
*                 dplyr::case_when(culmenlength < 35  ~ "small",
                                   culmenlength >= 35 & culmenlength <= 45 ~ "med", 
                                   culmenlength > 45 ~ "large")) %>%
  dplyr::slice(sample(1:nrow(.), size = 3))
```

```
## # A tibble: 3 × 5
##   individualid species             culmenlength culmenlength_cm culmenlenth_cla…
##   <chr>        <chr>                      <dbl>           <dbl> <chr>           
## 1 N62A1        Chinstrap penguin …         46.4            4.64 large           
## 2 N13A1        Adelie Penguin (Py…         38.8            3.88 med             
## 3 N92A1        Chinstrap penguin …         45.7            4.57 large
```

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# Wrangle data: _dplyr_

- `group_by()` to group by **column**

- `summarise()` to **summarize** each group

- `n()` to **count** observations within each group (context dependent)

```r
*(df_penguins_sum <- dplyr::group_by(df_penguins, island, species) %>%
* dplyr::summarise(n = dplyr::n(),
                   flipperlength_mn = mean(flipperlength), 
                   flipperlength_sd = sd(flipperlength), 
*                  .groups = "drop"))
```

```
## # A tibble: 5 × 5
##   island    species                          n flipperlength_mn flipperlength_sd
##   <chr>     <chr>                        <int>            <dbl>            <dbl>
## 1 Biscoe    Adelie Penguin (Pygoscelis …    44             189.             6.73
## 2 Biscoe    Gentoo penguin (Pygoscelis …   123             217.             6.48
## 3 Dream     Adelie Penguin (Pygoscelis …    56             190.             6.59
## 4 Dream     Chinstrap penguin (Pygoscel…    68             196.             7.13
## 5 Torgersen Adelie Penguin (Pygoscelis …    51             191.             6.23
```

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# Wrangle data: _dplyr_

- `*_join()` to combine columns from `x` and `y` using matching **keys**

```r
*dplyr::left_join(x = df_penguins, y = df_penguins_sum, by = c("island", "species")) %>%
  dplyr::select(species, island, tidyselect::starts_with("flipper")) %>% 
  dplyr::slice(sample(1:nrow(.), size = 5))
```

```
## # A tibble: 5 × 5
##   species                 island flipperlength flipperlength_mn flipperlength_sd
##   <chr>                   <chr>          <dbl>            <dbl>            <dbl>
## 1 Adelie Penguin (Pygosc… Dream            190             190.             6.59
## 2 Gentoo penguin (Pygosc… Biscoe           213             217.             6.48
## 3 Adelie Penguin (Pygosc… Biscoe           198             189.             6.73
## 4 Adelie Penguin (Pygosc… Biscoe           174             189.             6.73
## 5 Chinstrap penguin (Pyg… Dream            187             196.             7.13
```

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# Wrangle data: _dplyr_

.ref[https://statisticsglobe.com/r-dplyr-join-inner-left-right-full-semi-anti]

---

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# Tidy data: _tidyr_

- Create summarized `data.frame` with mean bodymass for each species, island, year

```r
(df_pen_sum <- dplyr::mutate(df_penguins, year = format(dateegg, "%Y"),
                             species = stringr::str_split(df_penguins$species, 
                                pattern = " ", simplify = TRUE)[, 1]) %>% 
  dplyr::group_by(species, island, year) %>% 
  dplyr::summarise(bodymass = mean(bodymass), .groups = "drop") %>% 
* dplyr::filter(year %in% c(2007, 2009)))
```

```
## # A tibble: 10 × 4
##    species   island    year  bodymass
##    <chr>     <chr>     <chr>    <dbl>
##  1 Adelie    Biscoe    2007     3620 
##  2 Adelie    Biscoe    2009     3858.
##  3 Adelie    Dream     2007     3671.
##  4 Adelie    Dream     2009     3651.
##  5 Adelie    Torgersen 2007     3763.
##  6 Adelie    Torgersen 2009     3489.
##  7 Chinstrap Dream     2007     3694.
##  8 Chinstrap Dream     2009     3725 
##  9 Gentoo    Biscoe    2007     5071.
## 10 Gentoo    Biscoe    2009     5141.
```

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# Tidy data: _tidyr_

- Reshape from **long** to **wide** specifying column names and values

```r
(df_pen_wide <- tidyr::pivot_wider(df_pen_sum, 
*                                  names_from = year, values_from = bodymass,
                                   names_prefix = "yr_") %>%
   dplyr::mutate(diff = (yr_2009 - yr_2007) / yr_2007 * 100))
```

```
## # A tibble: 5 × 5
##   species   island    yr_2007 yr_2009   diff
##   <chr>     <chr>       <dbl>   <dbl>  <dbl>
## 1 Adelie    Biscoe      3620    3858.  6.57 
## 2 Adelie    Dream       3671.   3651. -0.545
## 3 Adelie    Torgersen   3763.   3489. -7.28 
## 4 Chinstrap Dream       3694.   3725   0.833
## 5 Gentoo    Biscoe      5071.   5141.  1.38
```

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# Tidy data: _tidyr_

- Reshape from **wide** to **long** specifying which columns _not_ to reshape

```r
*tidyr::pivot_longer(df_pen_wide, -c(species, island),
                    names_to = "years", values_to = "bodymass") %>% 
  head(10)
```

```
## # A tibble: 10 × 4
##    species   island    years   bodymass
##    <chr>     <chr>     <chr>      <dbl>
##  1 Adelie    Biscoe    yr_2007 3620    
##  2 Adelie    Biscoe    yr_2009 3858.   
##  3 Adelie    Biscoe    diff       6.57 
##  4 Adelie    Dream     yr_2007 3671.   
##  5 Adelie    Dream     yr_2009 3651.   
##  6 Adelie    Dream     diff      -0.545
##  7 Adelie    Torgersen yr_2007 3763.   
##  8 Adelie    Torgersen yr_2009 3489.   
##  9 Adelie    Torgersen diff      -7.28 
## 10 Chinstrap Dream     yr_2007 3694.
```

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# Functional programming: _purrr_

- `map_*()` to apply function to each element (vector/list)

```r
species_names <- unique(df_penguins$species)
*purrr::map(species_names, function(i) {
  dplyr::filter(df_penguins, species == i) %>% dplyr::pull(island) %>% 
    unique() %>% stringr::str_sort() %>% paste(collapse = ", ")})
```

```
## [[1]]
## [1] "Biscoe, Dream, Torgersen"
## 
## [[2]]
## [1] "Biscoe"
## 
## [[3]]
## [1] "Dream"
```

```r
foo <- function(i) {dplyr::filter(df_penguins, species == i) %>%
    dplyr::pull(island) %>% unique %>% length}
*purrr::map_int(species_names, foo)
```

```
## [1] 3 1 1
```

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# Functional programming: _purrr_

- `reduce_*()` to **remove** a list by one level

```r
pick <- c("culmenlength", "culmendepth", "flipperlength")
foo <- function(i, j) {df <- dplyr::select(i, studyname, bodymass, j)
cor(df[, 2], df[, 3]) %>% dplyr::bind_cols(study = unique(df$studyname), .)
}

dplyr::group_by(df_penguins, island) %>% dplyr::group_split() %>% 
* purrr::map2(pick, foo) %>%
* purrr::reduce(left_join, by = "study")
```

```
## # A tibble: 3 × 4
##   study   culmenlength culmendepth flipperlength
##   <chr>          <dbl>       <dbl>         <dbl>
## 1 PAL0708        0.868       0.564         0.436
## 2 PAL0809        0.868       0.564         0.436
## 3 PAL0910        0.868       0.564         0.436
```

---

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## Thank you for your attention

### Questions?

.pull-left[

Further resources: [https://mhesselbarth.github.io/advanced-r-workshop/resources](https://mhesselbarth.github.io/advanced-r-workshop/resources)

Exercise: [https://mhesselbarth.github.io/advanced-r-workshop/exercise_tidyverse](https://mhesselbarth.github.io/advanced-r-workshop/exercise_tidyverse)

]

.pull-right[

]